深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向：generative_models_and_autoencoders
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 4,314 篇被归入该主题的论文。
主要结论
- 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
- NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
- 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
- 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
代表论文
按年份和会议均匀挑选，方便快速看到每一届的样貌
- NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
- ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
- ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
- CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
- NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
- ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
- ICLR 2025A Watermark for Order-Agnostic Language Models
- CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
- NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
- ICML 2024A Fixed-Point Approach for Causal Generative Modeling
- ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
- CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
- NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
- ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
- ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
- CVPR 20233D GAN Inversion With Facial Symmetry Prior
- NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
- ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
- ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
- CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back
全部深度学习 · generative models and autoencoders论文
共 830 篇，第 5 / 17 页
- ICLR 2026Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing
- ICLR 2026Partition Generative Modeling: Masked Modeling Without Masks
- ICLR 2026Pixel-Perfect Puppetry: Precision-Guided Enhancement for Face Image and Video Editing
- ICLR 2026PixNerd: Pixel Neural Field Diffusion
- ICLR 2026Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization
- ICLR 2026PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives
- ICLR 2026Practical estimation of the optimal classification error with soft labels and calibration
- ICLR 2026PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation
- ICLR 2026Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
- ICLR 2026Pretraining Scaling Laws for Generative Evaluations of Language Models
- ICLR 2026Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
- ICLR 2026Priors in time: Missing inductive biases for language model interpretability
- ICLR 2026Product-Quantised Image Representation for High-Quality Image Synthesis
- ICLR 2026PROTDYN: A FOUNDATION PROTEIN LANGUAGE MODEL FOR THERMODYNAMICS AND DYNAMICS GENERATION
- ICLR 2026ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
- ICLR 2026Provable Separations between Memorization and Generalization in Diffusion Models
- ICLR 2026Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Reference
- ICLR 2026Quasi-Monte Carlo Methods Enable Extremely Low-Dimensional Deep Generative Models
- ICLR 2026Quotient-Space Diffusion Model
- ICLR 2026ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
- ICLR 2026Riemannian Variational Flow Matching for Material and Protein Design
- ICLR 2026RNE: plug-and-play diffusion inference-time control and energy-based training
- ICLR 2026SAFER: Risk-Constrained Sample-then-Filter in Large Language Models
- ICLR 2026Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
- ICLR 2026Scaling Group Inference for Diverse and High-Quality Generation
- ICLR 2026Scaling Sequence-to-Sequence Generative Neural Rendering
- ICLR 2026Scaling Speech Tokenizers with Diffusion Autoencoders
- ICLR 2026ScalingCache: Extreme Acceleration of DiTs through Difference Scaling and Dynamic Interval Caching
- ICLR 2026SCOPED: Score–Curvature Out-of-distribution Proximity Evaluator for Diffusion
- ICLR 2026Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
- ICLR 2026Self-Speculative Decoding Accelerates Lossless Inference in Any-Order and Any-Subset Autoregressive Models
- ICLR 2026Self-Speculative Masked Diffusions
- ICLR 2026SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation
- ICLR 2026SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
- ICLR 2026SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
- ICLR 2026Shift-and-Sum Quantization for Visual Autoregressive Models
- ICLR 2026Shrinking Proteins with Diffusion
- ICLR 2026SIGMA-GEN: STRUCTURE AND IDENTITY GUIDED MULTI-SUBJECT ASSEMBLY FOR IMAGE GENERATION
- ICLR 2026Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
- ICLR 2026SimpleFold: Folding Proteins is Simpler than You Think
- ICLR 2026SimpleGVR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution
- ICLR 2026SoFlow: Solution Flow Models for One-Step Generative Modeling
- ICLR 2026SONA: Learning Conditional, Unconditional, and Matching-Aware Discriminator
- ICLR 2026SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
- ICLR 2026Sparse Autoencoders Trained on the Same Data Learn Different Features
- ICLR 2026Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
- ICLR 2026SparseD: Sparse Attention for Diffusion Language Models
- ICLR 2026SpatialHand: Generative Object Manipulation from 3D Prespective
- ICLR 2026Spatially Informed Autoencoders for Interpretable Visual Representation Learning
- ICLR 2026SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · generative models and autoencoders在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "dl-generative-models-and-autoencoders"}
常见问题
- 2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？
- 共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。
- NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？
- NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。
- ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。
- 哪个会议的深度学习 · generative models and autoencoders论文最多？
- NeurIPS，2023–2026 年共 1,714 篇。